





'''simple docstring'''






from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class 		lowerCamelCase	(   _A							):



	def 	_lowerCamelCase     ( self						):
			return [
			    {"col_1": 3, "col_2": "a"},
			    {"col_1": 2, "col_2": "b"},
			    {"col_1": 1, "col_2": "c"},
			    {"col_1": 0, "col_2": "d"},
			]



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
			return Dataset.from_dict(a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= self._create_example_records()
			lowerCAmelCase  :     Optional[int]       						= Dataset.from_list(a_						)
			self.assertListEqual(dset.column_names		,   ["col_1", "col_2"]						)
			for i, r in enumerate(a_						):
					self.assertDictEqual(a_		,   example_records[i]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= self._create_example_records()
			lowerCAmelCase  :     str       						= Dataset.from_list(a_						)
			lowerCAmelCase  :     Any       						= Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}						)
			self.assertEqual(dset.info		,   dset_from_dict.info						)



	def 	_lowerCamelCase     ( self						):  # checks what happens with missing columns
			lowerCAmelCase  :     str       						= [{"col_1": 1}, {"col_2": "x"}]
			lowerCAmelCase  :     str       						= Dataset.from_list(a_						)
			self.assertDictEqual(dset[0]		,   {"col_1": 1}						)
			self.assertDictEqual(dset[1]		,   {"col_1": None}						)  # NB: first record is used for columns



	def 	_lowerCamelCase     ( self						):  # checks if the type can be inferred from the second record
			lowerCAmelCase  :     int       						= [{"col_1": []}, {"col_1": [1, 2]}]
			lowerCAmelCase  :     Union[str, Any]       						= Dataset.from_list(a_						)
			self.assertEqual(dset.info.features["col_1"]		,   Sequence(Value("int64"						)						)						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= Dataset.from_list([]						)
			self.assertEqual(len(a_						)		,   0						)
			self.assertListEqual(dset.column_names		,   []						)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GraphormerForGraphClassification""",
					    """GraphormerModel""",
					    """GraphormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_graphormer import (
										    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GraphormerForGraphClassification,
										    GraphormerModel,
										    GraphormerPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



